The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges
- URL: http://arxiv.org/abs/2411.16691v1
- Date: Fri, 08 Nov 2024 23:36:16 GMT
- Title: The Dual Impact of Artificial Intelligence in Healthcare: Balancing Advancements with Ethical and Operational Challenges
- Authors: Balaji Shesharao Ingole, Vishnu Ramineni, Nikhil Kumar Pulipeta, Manoj Jayntilal Kathiriya, Manjunatha Sughaturu Krishnappa, Vivekananda Jayaram,
- Abstract summary: This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery.
Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.
- Score: 1.3302498881305604
- License:
- Abstract: The synchronic and diachronic study of the evolution of Artificial Intelligence (AI) unveils one prominent fact that its effect can be traced in almost all fields such as healthcare industry. The growth is perceived holistically in software, hardware implementation, or application in these various fields. As the title suggests, the review will highlight the impact of AI on healthcare possibly in all dimensions including precision medicine, diagnostics, drug development, automation of the process, etc., explicating whether AI is a blessing or a curse or both. With the availability of enough data and analysis to examine the topic at hand, however, its application is still functioning in quite early stages in many fields, the present work will endeavour to provide an answer to the question. This paper takes a close look at how AI is transforming areas such as diagnostics, precision medicine, and drug discovery, while also addressing some of the key ethical challenges it brings. Issues like patient privacy, safety, and the fairness of AI decisions are explored to understand whether AI in healthcare is a positive force, a potential risk, or perhaps both.
Related papers
- The Promise and Peril of Artificial Intelligence -- Violet Teaming
Offers a Balanced Path Forward [56.16884466478886]
This paper reviews emerging issues with opaque and uncontrollable AI systems.
It proposes an integrative framework called violet teaming to develop reliable and responsible AI.
It emerged from AI safety research to manage risks proactively by design.
arXiv Detail & Related papers (2023-08-28T02:10:38Z) - From Military to Healthcare: Adopting and Expanding Ethical Principles
for Generative Artificial Intelligence [10.577932700903112]
Generative AI, an emerging technology designed to efficiently generate valuable information, holds great promise.
We propose GREAT PLEA ethical principles, encompassing governance, reliability, equity, accountability, traceability, privacy, lawfulness, empathy, and autonomy, for generative AI in healthcare.
arXiv Detail & Related papers (2023-08-04T16:22:06Z) - It is not "accuracy vs. explainability" -- we need both for trustworthy
AI systems [0.0]
We are witnessing the emergence of an AI economy and society where AI technologies are increasingly impacting health care, business, transportation and many aspects of everyday life.
However, AI systems may produce errors, can exhibit bias, may be sensitive to noise in the data, and often lack technical and judicial transparency resulting in reduction in trust and challenges in their adoption.
These recent shortcomings and concerns have been documented in scientific but also in general press such as accidents with self driving cars, biases in healthcare, hiring and face recognition systems for people of color, seemingly correct medical decisions later found to be made due to wrong reasons etc.
arXiv Detail & Related papers (2022-12-16T23:33:10Z) - The Role of AI in Drug Discovery: Challenges, Opportunities, and
Strategies [97.5153823429076]
The benefits, challenges and drawbacks of AI in this field are reviewed.
The use of data augmentation, explainable AI, and the integration of AI with traditional experimental methods are also discussed.
arXiv Detail & Related papers (2022-12-08T23:23:39Z) - Breaking Bad News in the Era of Artificial Intelligence and Algorithmic
Medicine: An Exploration of Disclosure and its Ethical Justification using
the Hedonic Calculus [0.0]
We show how the 'Felicific Calculus' may have a timely quasi-quantitative application in the age of AI.
We show how this ethical algorithm can be used to assess, across seven mutually exclusive and exhaustive domains, whether an AI-supported action can be morally justified.
arXiv Detail & Related papers (2022-06-23T16:54:18Z) - Metaethical Perspectives on 'Benchmarking' AI Ethics [81.65697003067841]
Benchmarks are seen as the cornerstone for measuring technical progress in Artificial Intelligence (AI) research.
An increasingly prominent research area in AI is ethics, which currently has no set of benchmarks nor commonly accepted way for measuring the 'ethicality' of an AI system.
We argue that it makes more sense to talk about 'values' rather than 'ethics' when considering the possible actions of present and future AI systems.
arXiv Detail & Related papers (2022-04-11T14:36:39Z) - Artificial Intelligence in Healthcare: Lost In Translation? [0.0]
We highlight the major areas, where we observe current challenges for translation in AI in healthcare.
Our work will lead to improved translation of AI in healthcare products into the clinical setting.
arXiv Detail & Related papers (2021-07-28T16:10:40Z) - Trustworthy AI: A Computational Perspective [54.80482955088197]
We focus on six of the most crucial dimensions in achieving trustworthy AI: (i) Safety & Robustness, (ii) Non-discrimination & Fairness, (iii) Explainability, (iv) Privacy, (v) Accountability & Auditability, and (vi) Environmental Well-Being.
For each dimension, we review the recent related technologies according to a taxonomy and summarize their applications in real-world systems.
arXiv Detail & Related papers (2021-07-12T14:21:46Z) - Building Bridges: Generative Artworks to Explore AI Ethics [56.058588908294446]
In recent years, there has been an increased emphasis on understanding and mitigating adverse impacts of artificial intelligence (AI) technologies on society.
A significant challenge in the design of ethical AI systems is that there are multiple stakeholders in the AI pipeline, each with their own set of constraints and interests.
This position paper outlines some potential ways in which generative artworks can play this role by serving as accessible and powerful educational tools.
arXiv Detail & Related papers (2021-06-25T22:31:55Z) - Empowering Things with Intelligence: A Survey of the Progress,
Challenges, and Opportunities in Artificial Intelligence of Things [98.10037444792444]
We show how AI can empower the IoT to make it faster, smarter, greener, and safer.
First, we present progress in AI research for IoT from four perspectives: perceiving, learning, reasoning, and behaving.
Finally, we summarize some promising applications of AIoT that are likely to profoundly reshape our world.
arXiv Detail & Related papers (2020-11-17T13:14:28Z) - Explainable AI meets Healthcare: A Study on Heart Disease Dataset [0.0]
The aim is to enlighten practitioners on the understandability and interpretability of explainable AI systems using a variety of techniques.
Our paper contains examples based on the heart disease dataset and elucidates on how the explainability techniques should be preferred to create trustworthiness.
arXiv Detail & Related papers (2020-11-06T05:18:43Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.